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• 1. [Article] Two Studies Addressing Practical Needs of Wheat Farmers, Processors, and Breeders : Changes in Falling Number and Alpha-amylase During Grain Storage, and Improved Predictions of Wheat-flour Dough Properties

  Wheat (Triticum aestivum) is a globally traded staple food crop. The diverse and pleasing nature of wheat-derived products is a result of the complex interactions of the polymeric components from the wheat ...

  Citation × Citation Citation Abstract Copy Title: Two Studies Addressing Practical Needs of Wheat Farmers, Processors, and Breeders : Changes in Falling Number and Alpha-amylase During Grain Storage, and Improved Predictions of Wheat-flour Dough Properties Author: Adams, Mike R. Wheat (Triticum aestivum) is a globally traded staple food crop. The diverse and pleasing nature of wheat-derived products is a result of the complex interactions of the polymeric components from the wheat endosperm. Changes in the functionality of these polymeric components, as a result of changes in growing conditions or different genetics, impacts market price and end-product quality and directly affects farmers and processors. Wheat is of particular economic importance to the U.S. Pacific Northwest and, specifically, to the state of Oregon. Providing quality wheat for export is paramount to the survival of the Oregon wheat industry. This dissertation focuses on wheat quality from the perspective of serving the practical needs of farmers, processors, and wheat breeders. The first study, split into two portions, concerns pre-harvest sprouting (PHS) and grain storage. PHS increases alpha amylase (αA) activity in wheat, which, in excess, reduces wheat end-product quality. Falling Number (FN) is the primary test used by industry to gauge PHS damage in wheats. Direct measurement of αA activity is the fundamental frame of reference. The objective of these studies was to determine if FN and αA activity of wheat samples changed during storage and if changes were a function of storage time, storage temperature, and degree of PHS damage. Samples from three Idaho locations were used. These captured a wide range of PHS degree, and therefore, wide ranges of FN values and αA activities. Samples were subdivided and stored at -20°C, +20°C, and +40°C. Low FN values and high αA activities were observed in soft wheats from locations that had rain events prior to harvest. Overall, FN and αA activity had the curvilinear relationship expected from the literature, indicating the validity of the sample set with regard to the FN/αA relationship. Changes in FN and αA activity were observed over a 90 day period of grain storage. FN differed between growing environments, wheat varieties, and storage temperatures. αA activity also differed between growing environments and wheat varieties, but not between storage temperatures. Highest rates of increase in FN were observed in hard wheats with high initial (day 0) FN values. Lowest rates of increase in FN were observed in soft wheats with low day 0 FN values. This contrasted with the changes that occurred in αA activity. Decreases in αA activity over storage time were most prevalent in soft wheats, particularly sprouted soft wheats (i.e. those with day 0 αA activities > 0.1 Ceralpha Units: CU). There were small decreases in αA activity in hard wheats but the distinction between high and low αA activity samples was not as evident as in the soft wheats because the vast majority of hard wheat samples tested had αA activities < 0.1 CU. Decreases in αA activity were in general not associated with corresponding increases in FN values over grain storage time. Increases in FN values occurred at a higher rates as storage temperature increased, particularly in hard wheats with high day 0 FN. Grain storage was successful as a way to raise FN values to > 300 s in very few cases. Storage was not effective in decreasing αA activity from > 0.1 CU to < 0.1 CU. Increases in FN over storage time for the hard wheats significantly differed between locations. However, decreases in αA activities over storage time for the hard wheats were not significantly different between locations. This again highlights a lack of correspondence between increased FN and decreased αA activity, suggesting that these two factors are somewhat decoupled when looking at changes in stored grain. Temperature-induced gluten crosslinking was explored as possible explanation for drastic increases in unsprouted hard wheat FN observed in samples from one location. Total polymeric protein (TPP) content was assessed at the end of the study for unsprouted hard wheats stored at +40°C and -20°C as well as sprouted hard wheats from stored at -20°C. TPP content was assessed as % large unextractable polymeric proteins (%LUPP) and % total unextractable polymeric proteins (%TUPP) using size exclusion high performance liquid chromatography (HPSEC). TPP content was not significantly different between storage temperatures for wheat varieties from the same location. %TUPP, but not % LUPP, was significantly lower in wheat varieties affected by PHS. Changes in FN at high storage temperature were not likely due to increased protein crosslinking. The second study aimed to validate the use of a rapid method for predicting dough strength at early generations in hard wheat breeding programs. Early generation quality screening improves breeding program efficiency. Hard wheats are used to make leavened bread products. The gluten proteins, particularly high molecular weight glutenin subunits (HMW-GS), form large, ramifying networks called the glutenin macropolymer (GMP). High GMP content is associated with increased dough strength and bread quality. Genetic differences in HMW-GS, and by inference, GMP, are responsible for differing dough properties between varieties. The Mixograph is used to measure dough mixing properties and predict end-product quality in breeding programs. GMP can also be measured as total polymeric protein (TPP) via HPSEC. The Solvent Retention Capacity (SRC) test has been proposed to predict hard wheat quality, specifically lactic acid SRC (LASRC). The objectives of this research were to provide preliminary information on the usefulness of using LASRC, on its own, to predict dough mixing properties, specifically as applied to early generation screening in a wheat breeding program, and to assess the relationship between LASRC and TPP. Wheat samples were categorized by flour protein concentration (FPC). Mixograph analysis was used as the baseline for dough properties and was analyzed both by eye and by the proprietary Mixsmart software. TPP content was assessed as %LUPP and %%TUPP. As a result of redundancy between the two TPP measures, only %LUPP was used for statistical analysis. Dough mixing parameters were slightly better correlated with LASRC than %LUPP. Correlations between LASRC, %LUPP, and dough mixing parameters were different between FPC categories, particularly in low FPC samples. A strict cutoff of 115% LASRC effectively screened out the bottom 10% of low quality hard wheats but retained a nearly equal amount of low quality hard wheats that would have been screened out by mixograph analysis. LASRC and %LUPP are not likely to be effective predictors of dough properties, but may have some value to screen for hard wheat quality in the early generations of a wheat breeding cycle. Title: Two Studies Addressing Practical Needs of Wheat Farmers, Processors, and Breeders : Changes in Falling Number and Alpha-amylase During Grain Storage, and Improved Predictions of Wheat-flour Dough Properties Author: Adams, Mike R. Wheat (Triticum aestivum) is a globally traded staple food crop. The diverse and pleasing nature of wheat-derived products is a result of the complex interactions of the polymeric components from the wheat endosperm. Changes in the functionality of these polymeric components, as a result of changes in growing conditions or different genetics, impacts market price and end-product quality and directly affects farmers and processors. Wheat is of particular economic importance to the U.S. Pacific Northwest and, specifically, to the state of Oregon. Providing quality wheat for export is paramount to the survival of the Oregon wheat industry. This dissertation focuses on wheat quality from the perspective of serving the practical needs of farmers, processors, and wheat breeders. The first study, split into two portions, concerns pre-harvest sprouting (PHS) and grain storage. PHS increases alpha amylase (αA) activity in wheat, which, in excess, reduces wheat end-product quality. Falling Number (FN) is the primary test used by industry to gauge PHS damage in wheats. Direct measurement of αA activity is the fundamental frame of reference. The objective of these studies was to determine if FN and αA activity of wheat samples changed during storage and if changes were a function of storage time, storage temperature, and degree of PHS damage. Samples from three Idaho locations were used. These captured a wide range of PHS degree, and therefore, wide ranges of FN values and αA activities. Samples were subdivided and stored at -20°C, +20°C, and +40°C. Low FN values and high αA activities were observed in soft wheats from locations that had rain events prior to harvest. Overall, FN and αA activity had the curvilinear relationship expected from the literature, indicating the validity of the sample set with regard to the FN/αA relationship. Changes in FN and αA activity were observed over a 90 day period of grain storage. FN differed between growing environments, wheat varieties, and storage temperatures. αA activity also differed between growing environments and wheat varieties, but not between storage temperatures. Highest rates of increase in FN were observed in hard wheats with high initial (day 0) FN values. Lowest rates of increase in FN were observed in soft wheats with low day 0 FN values. This contrasted with the changes that occurred in αA activity. Decreases in αA activity over storage time were most prevalent in soft wheats, particularly sprouted soft wheats (i.e. those with day 0 αA activities > 0.1 Ceralpha Units: CU). There were small decreases in αA activity in hard wheats but the distinction between high and low αA activity samples was not as evident as in the soft wheats because the vast majority of hard wheat samples tested had αA activities < 0.1 CU. Decreases in αA activity were in general not associated with corresponding increases in FN values over grain storage time. Increases in FN values occurred at a higher rates as storage temperature increased, particularly in hard wheats with high day 0 FN. Grain storage was successful as a way to raise FN values to > 300 s in very few cases. Storage was not effective in decreasing αA activity from > 0.1 CU to < 0.1 CU. Increases in FN over storage time for the hard wheats significantly differed between locations. However, decreases in αA activities over storage time for the hard wheats were not significantly different between locations. This again highlights a lack of correspondence between increased FN and decreased αA activity, suggesting that these two factors are somewhat decoupled when looking at changes in stored grain. Temperature-induced gluten crosslinking was explored as possible explanation for drastic increases in unsprouted hard wheat FN observed in samples from one location. Total polymeric protein (TPP) content was assessed at the end of the study for unsprouted hard wheats stored at +40°C and -20°C as well as sprouted hard wheats from stored at -20°C. TPP content was assessed as % large unextractable polymeric proteins (%LUPP) and % total unextractable polymeric proteins (%TUPP) using size exclusion high performance liquid chromatography (HPSEC). TPP content was not significantly different between storage temperatures for wheat varieties from the same location. %TUPP, but not % LUPP, was significantly lower in wheat varieties affected by PHS. Changes in FN at high storage temperature were not likely due to increased protein crosslinking. The second study aimed to validate the use of a rapid method for predicting dough strength at early generations in hard wheat breeding programs. Early generation quality screening improves breeding program efficiency. Hard wheats are used to make leavened bread products. The gluten proteins, particularly high molecular weight glutenin subunits (HMW-GS), form large, ramifying networks called the glutenin macropolymer (GMP). High GMP content is associated with increased dough strength and bread quality. Genetic differences in HMW-GS, and by inference, GMP, are responsible for differing dough properties between varieties. The Mixograph is used to measure dough mixing properties and predict end-product quality in breeding programs. GMP can also be measured as total polymeric protein (TPP) via HPSEC. The Solvent Retention Capacity (SRC) test has been proposed to predict hard wheat quality, specifically lactic acid SRC (LASRC). The objectives of this research were to provide preliminary information on the usefulness of using LASRC, on its own, to predict dough mixing properties, specifically as applied to early generation screening in a wheat breeding program, and to assess the relationship between LASRC and TPP. Wheat samples were categorized by flour protein concentration (FPC). Mixograph analysis was used as the baseline for dough properties and was analyzed both by eye and by the proprietary Mixsmart software. TPP content was assessed as %LUPP and %%TUPP. As a result of redundancy between the two TPP measures, only %LUPP was used for statistical analysis. Dough mixing parameters were slightly better correlated with LASRC than %LUPP. Correlations between LASRC, %LUPP, and dough mixing parameters were different between FPC categories, particularly in low FPC samples. A strict cutoff of 115% LASRC effectively screened out the bottom 10% of low quality hard wheats but retained a nearly equal amount of low quality hard wheats that would have been screened out by mixograph analysis. LASRC and %LUPP are not likely to be effective predictors of dough properties, but may have some value to screen for hard wheat quality in the early generations of a wheat breeding cycle. Close

• 2. [Article] Influence of Soft Wheat Characteristics on Quality of Batter-based Products

  Wheat is a globally traded staple crop. Wheat is important in human diets because of its agronomic adaptability, physical characteristics, functionality for the production of leavened products and nutritional ...

  Citation × Citation Citation Abstract Copy Title: Influence of Soft Wheat Characteristics on Quality of Batter-based Products Author: Fajardo Centeno, Carlos A. Wheat is a globally traded staple crop. Wheat is important in human diets because of its agronomic adaptability, physical characteristics, functionality for the production of leavened products and nutritional value. Two significant characteristics make wheat an important staple food-crop. First, the proteins present in wheat endosperm have attributes that enable gas retention after the proteins are hydrated and mechanically worked during dough production. Second, a wider variety of products can be made out of wheat compared to other cereals. Wheat quality is defined in terms of suitability for specific end-uses. This is important for breeders, farmers, flour millers, and food producers and consumers. In the U.S. Pacific Northwest (PNW) climatic conditions favor production of soft wheat. Three soft wheat types are planted in the PNW, soft white winter (SWW), soft white spring (SWS), and club (CLUB). Batter-based products are important applications for soft wheats and include a wide range of products such as pancakes and waffles, cakes, and coatings. Pancakes are produced from fluid batters using a single step mixing process and contain sugar concentrations < 30% in their formulations. Cakes are complex food systems where their classification is based on mixing process to produce the batters and the sugar-flour ratio concentrations in their formulations. This dissertation is focused on the functionality, analysis, and selection of soft wheat quality traits that affect end-product performance and also developing a methodology to rapidly predict cake quality. The first study (Chapter 3) was concerned with the functionality of SWW wheats in pancake making. The aim of this study was to observe the differences in genotype and protein concentration on batter flow and pancake making performance of a collection of SWW wheats. Two formulations were used in the study: one based on Finnie et al (2006) called "old" and another based on the AACC-I Approved Method 10-80.01 called "new". The "new" lean formulation had an improved ability to distinguish the performance of different flours compared with the “old” as a result of wider range of pancake diameters. This study showed that pancake making performance would not be optimized by conventional superior high-quality soft wheat flours with soft kernel texture, high break flour yield, and low water-, carbonate-, and sucrose SRCs. From our results it appears that for unchlorinated flours, at least for thicker pancakes, the most appropriate flour would have higher water and sucrose SRCs and be grown under management conditions conductive to higher protein. The second study (Chapter 4) was a meta-analysis of data collected by the USDA Western Wheat Quality Laboratory (Pullman, WA). This study was done to advance understanding soft wheat quality traits that differentially affect sugar-snap cookie diameter (CODI) and Japanese sponge cake (SC) volume (CAVOL). Principal component analysis (PCA) and partial least square (PLS) regression models were used to obtain useful actionable information from the data. The overall data showed that break flour yield (BKFY) was the single most important trait positively associated with both CAVOL and CODI. SWW wheats showed CVs > 10% for kernel hardness (SKHRD), grain and flour protein concentrations, ash, sucrose-, and lactic acid SRCs. These observations suggested that hardness, protein, ash, and the two SRCs were more sensitive to G&E effects than were the end-product traits that had CVs < 10%. The third study (Chapter 5) was built on the second study by adding two additional quality tests, oxidative gelation capacity (PeakOXI) and median particle size, to the potential prediction of CODI and CAVOL. Similar to the second study, BKFY was the single most important trait positively associated with both CAVOL and CODI. Virtual selection of SWWs based on either BKFY or SKHRD alone showed (in both the second and third studies) that using these enabled a gain of 134 mL for CAVOL and 0.6 cm for CODI using SKHRD and 122 mL for CAVOL and 0.58 cm for CODI using BKFY (Chapter 5). PeakOXI was significantly correlated with CODI but not with CAVOL. This contrasted with our hypothesis that PeakOXI would affect both products similarly. Notably 13 SWW samples had PeakOXI values higher than 800 cP. PeakOXI values this high have never been observed in soft wheats prior to this study. This is a valuable genetic resource for further studies that may lead to ways to better exploit oxidative gelation. The fourth study (Chapter 6) expanded the concepts in previous studies and included the use of a test to measure cake-batter viscosity in an attempt to predict cake quality. This study investigated the relationships between wheat quality traits, cake batters, and cake making quality in three cake types: SC, layer cake (LC), and pound cake (PoC). This study differed from the studies in Chapters 4 and 5 and was similar to Chapter 3 as the samples were fewer but specifically chosen to span the entire range of typical SWW quality. In this study we also developed a viscosity-based method to predict SC and LC quality that takes only eight minutes. This could be useful for screening or selection for cake quality in soft wheat breeding programs. In SC, there were no significant differences in cake quality traits between varieties. However, SC volume had a strong negative association with PeakOXI. For LC, the variety Tubbs, with harder kernel and higher absorption characteristics, had the largest LC volume. In contrast to SC, LC volume was significantly and positively associated with PeakOXI. In PoC, Kaseburg, with the highest protein content, had the largest cake volume. PoC was significantly and positively associated with flour protein concentration suggesting that flour proteins were important for larger volumes and confirming other observations in the literature. In contrast to LC and SC, PoC was not significantly associated with PeakOXI. The overall impact of the studies reported is: - For pancakes, the most important soft wheat trait is flour protein concentration. Water-, and sucrose SRCs were potentially useful parameters for predicting pancake quality. - For SC and sugar-snap cookies, break flour yield was the most important single trait in predicting higher SC volumes and larger cookie diameters. Therefore, selection in soft wheat breeding should be focused on kernel hardness and break flour yields as primary factors. Title: Influence of Soft Wheat Characteristics on Quality of Batter-based Products Author: Fajardo Centeno, Carlos A. Wheat is a globally traded staple crop. Wheat is important in human diets because of its agronomic adaptability, physical characteristics, functionality for the production of leavened products and nutritional value. Two significant characteristics make wheat an important staple food-crop. First, the proteins present in wheat endosperm have attributes that enable gas retention after the proteins are hydrated and mechanically worked during dough production. Second, a wider variety of products can be made out of wheat compared to other cereals. Wheat quality is defined in terms of suitability for specific end-uses. This is important for breeders, farmers, flour millers, and food producers and consumers. In the U.S. Pacific Northwest (PNW) climatic conditions favor production of soft wheat. Three soft wheat types are planted in the PNW, soft white winter (SWW), soft white spring (SWS), and club (CLUB). Batter-based products are important applications for soft wheats and include a wide range of products such as pancakes and waffles, cakes, and coatings. Pancakes are produced from fluid batters using a single step mixing process and contain sugar concentrations < 30% in their formulations. Cakes are complex food systems where their classification is based on mixing process to produce the batters and the sugar-flour ratio concentrations in their formulations. This dissertation is focused on the functionality, analysis, and selection of soft wheat quality traits that affect end-product performance and also developing a methodology to rapidly predict cake quality. The first study (Chapter 3) was concerned with the functionality of SWW wheats in pancake making. The aim of this study was to observe the differences in genotype and protein concentration on batter flow and pancake making performance of a collection of SWW wheats. Two formulations were used in the study: one based on Finnie et al (2006) called "old" and another based on the AACC-I Approved Method 10-80.01 called "new". The "new" lean formulation had an improved ability to distinguish the performance of different flours compared with the “old” as a result of wider range of pancake diameters. This study showed that pancake making performance would not be optimized by conventional superior high-quality soft wheat flours with soft kernel texture, high break flour yield, and low water-, carbonate-, and sucrose SRCs. From our results it appears that for unchlorinated flours, at least for thicker pancakes, the most appropriate flour would have higher water and sucrose SRCs and be grown under management conditions conductive to higher protein. The second study (Chapter 4) was a meta-analysis of data collected by the USDA Western Wheat Quality Laboratory (Pullman, WA). This study was done to advance understanding soft wheat quality traits that differentially affect sugar-snap cookie diameter (CODI) and Japanese sponge cake (SC) volume (CAVOL). Principal component analysis (PCA) and partial least square (PLS) regression models were used to obtain useful actionable information from the data. The overall data showed that break flour yield (BKFY) was the single most important trait positively associated with both CAVOL and CODI. SWW wheats showed CVs > 10% for kernel hardness (SKHRD), grain and flour protein concentrations, ash, sucrose-, and lactic acid SRCs. These observations suggested that hardness, protein, ash, and the two SRCs were more sensitive to G&E effects than were the end-product traits that had CVs < 10%. The third study (Chapter 5) was built on the second study by adding two additional quality tests, oxidative gelation capacity (PeakOXI) and median particle size, to the potential prediction of CODI and CAVOL. Similar to the second study, BKFY was the single most important trait positively associated with both CAVOL and CODI. Virtual selection of SWWs based on either BKFY or SKHRD alone showed (in both the second and third studies) that using these enabled a gain of 134 mL for CAVOL and 0.6 cm for CODI using SKHRD and 122 mL for CAVOL and 0.58 cm for CODI using BKFY (Chapter 5). PeakOXI was significantly correlated with CODI but not with CAVOL. This contrasted with our hypothesis that PeakOXI would affect both products similarly. Notably 13 SWW samples had PeakOXI values higher than 800 cP. PeakOXI values this high have never been observed in soft wheats prior to this study. This is a valuable genetic resource for further studies that may lead to ways to better exploit oxidative gelation. The fourth study (Chapter 6) expanded the concepts in previous studies and included the use of a test to measure cake-batter viscosity in an attempt to predict cake quality. This study investigated the relationships between wheat quality traits, cake batters, and cake making quality in three cake types: SC, layer cake (LC), and pound cake (PoC). This study differed from the studies in Chapters 4 and 5 and was similar to Chapter 3 as the samples were fewer but specifically chosen to span the entire range of typical SWW quality. In this study we also developed a viscosity-based method to predict SC and LC quality that takes only eight minutes. This could be useful for screening or selection for cake quality in soft wheat breeding programs. In SC, there were no significant differences in cake quality traits between varieties. However, SC volume had a strong negative association with PeakOXI. For LC, the variety Tubbs, with harder kernel and higher absorption characteristics, had the largest LC volume. In contrast to SC, LC volume was significantly and positively associated with PeakOXI. In PoC, Kaseburg, with the highest protein content, had the largest cake volume. PoC was significantly and positively associated with flour protein concentration suggesting that flour proteins were important for larger volumes and confirming other observations in the literature. In contrast to LC and SC, PoC was not significantly associated with PeakOXI. The overall impact of the studies reported is: - For pancakes, the most important soft wheat trait is flour protein concentration. Water-, and sucrose SRCs were potentially useful parameters for predicting pancake quality. - For SC and sugar-snap cookies, break flour yield was the most important single trait in predicting higher SC volumes and larger cookie diameters. Therefore, selection in soft wheat breeding should be focused on kernel hardness and break flour yields as primary factors. Close

• 3. [Article] Oxidative gelation and functionality of wheat flour : effects of grain storage, flour aging, and grain type (hard or soft)

  This study examined changes in flour functionality during storage of grain and subsequent aging of flour milled from the grain. Freshly harvested grain was stored for 24 weeks and flour milled from the ...

  Citation × Citation Citation Abstract Copy Title: Oxidative gelation and functionality of wheat flour : effects of grain storage, flour aging, and grain type (hard or soft) Author: Mattson, Jacob E. This study examined changes in flour functionality during storage of grain and subsequent aging of flour milled from the grain. Freshly harvested grain was stored for 24 weeks and flour milled from the grain at specified time intervals after harvest (0, 3, 6, 12, and 24 weeks). For each milling date flour functionality was measured on the day of milling (day 0) and at specified intervals after milling (1, 3, 6, 13, 27, and 62 days). Storage and aging were conducted at 23 ± 1°C. The functional properties examined were flour absorption characteristics, quantified using the solvent retention capacity (SRC) test, and oxidative gelation capacity (OGC), which was measured viscometrically, using a Rapid Visco Analyzer (RVA). SRC measures absorption capacity of flour emphasizing different flour polymers in each of the four solvents (water, all polymers: sucrose, arabinoxylans (AX) and gliadins: sodium carbonate, damaged starch: lactic acid, glutenins). Oxidative gelation is a process whereupon a weak gel is formed in a hydrated flour system under oxidative conditions and is thought to be largely a function of the reactivity of ferulic acid residues esterified to AX, although proteins are also involved. To determine if oxidation of flour lipids might accompany changes in OGC, the concentration of malondialdehyde (MDA), a lipid oxidation byproduct, was measured. Four soft-wheat varieties with divergent functionalities were selected to examine the effects of storage and aging. Additionally, a survey was conducted to examine the range of OGC in a selection of hard-grained wheat varieties from the Oregon State University wheat-breeding program. From this survey, one variety with high and one variety with low OGC were selected for a pilot study to determine the effect of hydrogen peroxide and azodicarbonamide (ADA) concentrations on OGC in straight grade flour. Variety was the strongest factor in determining flour functionality expressed as SRC and OGC. This is unsurprising, because varieties were chosen based on differences in absorption characteristics and OGC as indicated by preliminary testing. In contrast, variety was the weakest factor in determining changes in MDA concentration. As a function of grain storage time, water, sucrose and sodium carbonate SRC values increased. In contrast, lactic acid SRC values declined. Although many of these changes were statistically significant, their functional significance remains unclear. As a function of grain storage time, OGC initially increased to week 3 then declined to week 24. Not only was this change statistically significant, but the magnitude of the change could be considered functionally significant. Because OGC is a trait that currently only has theoretical value in food processing (i.e. OGC is not a trait currently taken into consideration during food processing), it is difficult to definitively conclude what constitutes functional significance. Grain storage time had the strongest influence on changes in MDA concentration. The trend of changes in MDA concentration was similar to that observed for OGC. Flour age was the weakest contributor to changes in SRC. Looking at individual SRC solvents, flour aging time did not significantly influence changes in water SRC values. However, as flour aged, sucrose SRC values significantly increased and sodium carbonate and lactic acid SRC values decreased. Although changes in sucrose, sodium carbonate, and lactic acid SRCs were statistically significant, their functional significance was again unclear. As a function of flour age, OGC increased. As a function of flour age, MDA concentration initially increased, but subsequently declined and remained constant from day 6 to day 62. Each variety appeared to show a different relationship between peroxide peak viscosity (PPV) and peroxide peak breakdown viscosity (PPBV). Proportional PPBV also appeared to differ between varieties, and the relationships (PPBV% vs PPV) were nonlinear. This suggested that there was a maximum PPV for each variety at which PPBV no longer increased. Data suggest qualitative differences in the gels formed in each variety that require further investigation. Speculation allows the idea that the RVA method used here could provide a way of expressing functional differences in OGC that might relate to structure differences in AX. Title: Oxidative gelation and functionality of wheat flour : effects of grain storage, flour aging, and grain type (hard or soft) Author: Mattson, Jacob E. This study examined changes in flour functionality during storage of grain and subsequent aging of flour milled from the grain. Freshly harvested grain was stored for 24 weeks and flour milled from the grain at specified time intervals after harvest (0, 3, 6, 12, and 24 weeks). For each milling date flour functionality was measured on the day of milling (day 0) and at specified intervals after milling (1, 3, 6, 13, 27, and 62 days). Storage and aging were conducted at 23 ± 1°C. The functional properties examined were flour absorption characteristics, quantified using the solvent retention capacity (SRC) test, and oxidative gelation capacity (OGC), which was measured viscometrically, using a Rapid Visco Analyzer (RVA). SRC measures absorption capacity of flour emphasizing different flour polymers in each of the four solvents (water, all polymers: sucrose, arabinoxylans (AX) and gliadins: sodium carbonate, damaged starch: lactic acid, glutenins). Oxidative gelation is a process whereupon a weak gel is formed in a hydrated flour system under oxidative conditions and is thought to be largely a function of the reactivity of ferulic acid residues esterified to AX, although proteins are also involved. To determine if oxidation of flour lipids might accompany changes in OGC, the concentration of malondialdehyde (MDA), a lipid oxidation byproduct, was measured. Four soft-wheat varieties with divergent functionalities were selected to examine the effects of storage and aging. Additionally, a survey was conducted to examine the range of OGC in a selection of hard-grained wheat varieties from the Oregon State University wheat-breeding program. From this survey, one variety with high and one variety with low OGC were selected for a pilot study to determine the effect of hydrogen peroxide and azodicarbonamide (ADA) concentrations on OGC in straight grade flour. Variety was the strongest factor in determining flour functionality expressed as SRC and OGC. This is unsurprising, because varieties were chosen based on differences in absorption characteristics and OGC as indicated by preliminary testing. In contrast, variety was the weakest factor in determining changes in MDA concentration. As a function of grain storage time, water, sucrose and sodium carbonate SRC values increased. In contrast, lactic acid SRC values declined. Although many of these changes were statistically significant, their functional significance remains unclear. As a function of grain storage time, OGC initially increased to week 3 then declined to week 24. Not only was this change statistically significant, but the magnitude of the change could be considered functionally significant. Because OGC is a trait that currently only has theoretical value in food processing (i.e. OGC is not a trait currently taken into consideration during food processing), it is difficult to definitively conclude what constitutes functional significance. Grain storage time had the strongest influence on changes in MDA concentration. The trend of changes in MDA concentration was similar to that observed for OGC. Flour age was the weakest contributor to changes in SRC. Looking at individual SRC solvents, flour aging time did not significantly influence changes in water SRC values. However, as flour aged, sucrose SRC values significantly increased and sodium carbonate and lactic acid SRC values decreased. Although changes in sucrose, sodium carbonate, and lactic acid SRCs were statistically significant, their functional significance was again unclear. As a function of flour age, OGC increased. As a function of flour age, MDA concentration initially increased, but subsequently declined and remained constant from day 6 to day 62. Each variety appeared to show a different relationship between peroxide peak viscosity (PPV) and peroxide peak breakdown viscosity (PPBV). Proportional PPBV also appeared to differ between varieties, and the relationships (PPBV% vs PPV) were nonlinear. This suggested that there was a maximum PPV for each variety at which PPBV no longer increased. Data suggest qualitative differences in the gels formed in each variety that require further investigation. Speculation allows the idea that the RVA method used here could provide a way of expressing functional differences in OGC that might relate to structure differences in AX. Close